“The important thing is not to stop questioning. Curiosity has its own reason for existing…” — Albert Einstein
How does your organisation make key decisions?
Few would argue that making business decisions can be critical and individual decisions can affect the success of an organisation. Often, the decisions can be difficult to make because there is no obvious “right” and “wrong” answer, and organisations instead must focus on making the most right decision; for example, the one that will deliver the best return on investment or the most value to its customers. Informed decision making of this type relies on having a good understanding of the business environment, context and operations. Access to analytical data becomes essential, and it’s easy to assume that “access to more data is better”.
However, as organisations collect more data from more sources, and as the speed and availability of that data increases, a real challenge emerges. How can decision makers use information and data effectively, without drowning in a sea of ever-changing numbers and figures? Having a high volume of data, with no way of efficiently and effectively interpreting it can lead to “analysis-paralysis”. In a worst case scenario it can slow down decision making to a snail’s pace, as decision makers struggle to interpret what various disparate data sources are telling them.
I recently saw an interesting video where Wendy Harrington (Chief Marketing Officer and EVP at Franklin Templeton Investments) spoke about the challenge of mining data. She spoke of not only the challenges relating to the volume, but also the velocity of the data and the need for hypothesis based approach, along with healthy curiosity. She also spoke about linking data and insight into customer value. These are important themes that are well worth consideration when you are analysing your data:
- Hypothesis based approach: However complex your analysis model, it’s unlikely that you’re ever going to accurately model your entire business ecosystem and anticipate human behaviour. It’s easy to confuse correlation with causation… taking a hypothesis based approach encourages the isolation of trends, insight as well as the monitoring and testing of that over time. (The six-sigma technic DMAIC – Define, Measure, Analyse, Improve, Control can be useful here)
- Customer value: It’s tempting to get caught up in collecting and monitoring data because the capability to do so exists. It’s well worth considering which metrics are most meaningful, and focusing on the ones that really drive business value and customer value. This enables you to link the hypotheses you form to customer and business value.
- Healthy Curiosity: Finally, nothing beats healthy curiosity. If data is being used to drive genuine insight, then curiosity about the customer (and their experience) is essential. Could there be an unexpected cause to the ‘data blip’ that you observed? Is there any way to test for that? Could this indicate that there’s a better way of serving the customer’s needs?
The need for healthy curiosity that Wendy Harrington described really resonated with me; I haven’t heard it articulated that way before, but it is an extremely eloquent metaphor.
Conclusion: Ensure your analytic capability allows healthy curiosity
However large your business—whether you’re a small, mid-size or multinational organisation—ensuring that your analytic capability enables you to have healthy curiosity, and the ability to test granular hypotheses will pay dividends in the long run.
This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet.